How climate change will affect overwintering crops is largely unknown due to the complex and understudied interactions among temperature, rainfall and snowpack. Increases in average winter temperature should release cold limitations yet warming-induced reductions of snowpack thickness should lead to decreased insulation effects and more exposure to freezing. Here, using statistical models, we show that the presence of snowpack weakens yield sensitivity to freezing stress by 22% during 1999–2019. By 2080–2100, we project that reduced snow cover insulation will offset up to one-third of the yield benefit (8.8 ± 1.1% for RCP 4.5 and 11.8 ± 1.4% for RCP 8.5) from reduced frost stress across the United States. Furthermore, by 2080–2100 future decline in wheat growing season snowfall (source of snowmelt) will drive a yield loss greater than the yield benefit from increasing rainfall. Explicitly considering these factors is critical to predict the climate change impacts on winter wheat production in snowy regions.
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All data used in this study are from publicly available datasets. US winter wheat yield, planted area and production data at county level are available at https://quickstats.nass.usda.gov/. The PRISM daily spatial climate dataset is accessed through Google Earth Engine at ee.ImageCollection. Daily snow depth data are available at https://doi.org/10.5067/W9FOYWH0EQZ3. AgMIP phase 1 model outputs are available at http://www.rdcep.org/research-projects/ggcmi. Processed and extracted variables used directly in the analyses are available at https://doi.org/10.5281/zenodo.6209590
The scripts and datasets used to run the regression and generate tables and figures are also available through Zenodo at https://doi.org/10.5281/zenodo.6209590.
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This work is partially supported by the USDA National Institute of Food and Agriculture Specialty Crop Research Initiative to Z.J. under award no. 2021-51181-35861. P.Z. and P.C. are supported by the CLAND project (grant no. 16-CONV-0003) and ISIPEDIA: The Open Inter-Sectoral Impacts Encyclopedia (grant no. ANR-17-ERA4-0006—ISIPEDIA). D.M. is supported by the CLAND project (grant no. 16-CONV-0003) and meta-program CLIMAE-INRAE. C.L. is supported by the MnDRIVE Informatics PhD Graduate Fellowship.
The authors declare no competing interests.
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Extended Data Fig. 1 Comparison of yield sensitivity to rainfall and snowfall between crop models and yield statistics.
Comparison of estimated coefficients of rainfall and snowfall on wheat yield estimated based on process-based crop model simulations (seven different process-based crop models considered) and yield statistics (‘observation’). Each bar represents estimated coefficients derived from process-based crop models. Horizontal lines represent the estimated coefficients derived from this study.
Yield changes due to changes in FDD, snow cover fraction, GDD, rainfall, and snowfall with no growing season shift (a), −15 days growing season shift (b) and +15 days growing season shift (c) in the future. Yield impacts of climate change were estimated as the ensemble mean of yield impacts projected with nine GCM and then weighted by county planting areas. Error bars indicate the 95% confidence interval of each estimation.
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Zhu, P., Kim, T., Jin, Z. et al. The critical benefits of snowpack insulation and snowmelt for winter wheat productivity. Nat. Clim. Chang. 12, 485–490 (2022). https://doi.org/10.1038/s41558-022-01327-3
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